A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications
Wang, Kunfeng1; Liu, Yuqiang1,2; Gou, Chao1,3; Wang, Fei-Yue1,4; Wang, Kunfeng(王坤峰)
2016-06-01
发表期刊IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷号65期号:6页码:4144-4158
文章类型Article
摘要This paper proposes an effective multi-view learning approach to foreground detection for traffic surveillance applications. This approach involves three main steps. First, a reference background image is generated via temporal median filtering, and multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation, each of which represents a unique view) are extracted from the video sequence. Then, a multi-view learning strategy is devised to online estimate the conditional probability densities for both the foreground and the background. The probability densities of three features are approximately conditionally independent and are estimated with kernel density estimation. Pixel soft labeling is conducted by using Bayes rule, and the pixelwise foreground posteriors are computed. Finally, a Markov random field is constructed to incorporate the spatiotemporal context into the foreground/background decision model. The belief propagation algorithm is used to label each pixel of the current frame. Experimental results verify that the proposed approach is effective to detect foreground objects from challenging traffic environments and outperforms some state-of-the-art methods.
关键词Conditional Independence Foreground Detection Heterogeneous Features Markov Random Field (Mrf) Multi-view Learning
WOS标题词Science & Technology ; Technology
学科领域Civil Engineering
DOI10.1109/TVT.2015.2509465
关键词[WOS]GAUSSIAN MIXTURE MODEL ; REAL-TIME TRACKING ; BACKGROUND SUBTRACTION ; OBJECT DETECTION ; VISUAL SURVEILLANCE ; CAST SHADOWS ; SEGMENTATION ; SUPPRESSION ; EDGE
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收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61304200) ; MIIT Project of Internet of Things Development Fund(1F15E02)
WOS研究方向Engineering ; Telecommunications ; Transportation
WOS类目Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS记录号WOS:000380068500026
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10859
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
通讯作者Wang, Kunfeng(王坤峰)
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.China Acad Railway Sci, Beijing 100081, Peoples R China
3.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China
4.Natl Univ Def Technol, Res Ctr Computat Expt & Parallel Syst, Changsha 410073, Hunan, Peoples R China
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Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,et al. A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2016,65(6):4144-4158.
APA Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,Wang, Fei-Yue,&Wang, Kunfeng.(2016).A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,65(6),4144-4158.
MLA Wang, Kunfeng,et al."A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 65.6(2016):4144-4158.
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